Senior Data Analyst – Controlled Environments

Syngenta Group•Nampa, ID

1d•$88,000 - $132,000•Onsite

About The Position

The Data Analyst enables reliable, data-driven decisions across the Trait Introgression (TI) and Double Haploid (DH) pipelines at the Trait Conversion Accelerator (TCA) in Nampa, Idaho. The role transforms operational, phenotypic, genotypic, and inventory data into trusted insights that strengthen operational excellence, productivity, traceability, and delivery. The position defines and governs data architecture, integrates diverse data sources into scalable analytical workflows, and develops reporting tools that support planning, project prioritization, workload and budget management, resource allocation, and continuous improvement. It combines programming and analytical expertise with an understanding of crop biological systems to convert complex data into clear performance indicators and actionable recommendations for Field Crops controlled-environment operations.

Requirements

M.S. or Ph.D. in Data Science, Data Engineering, Statistics, Industrial Engineering, or a related field.
Minimum 2 years of experience in data analytics, statistics, computational biology, or a related field is preferred.
Experience in data manipulation, integration, analysis, and interpretation; graphic summaries and reporting; and dashboard development and management for visualization and communication.
Ability to translate complex analytical outputs into concise, action-driven recommendations.
Experience with Microsoft-based data tools or related analytics platforms.
Familiarity with data-collection devices and process improvement.
Must reside in and be permanently authorized to work in the United States without current or future employer sponsorship.

Nice To Haves

Experience modeling biological systems and developing simulation and predictive algorithms, including AI-based algorithms, is highly desired.
Experience in data analytics, statistics, computational biology, or a related field.

Responsibilities

Design, develop, and maintain trusted datasets, scalable dashboards, and recurring reports supporting DH and TI workflows from project initiation through controlled-environment execution, genotyping, phenotyping, selection, advancement, and hand-off.
Define, monitor, and report operational and business KPIs across DH and TI execution pipelines; compare results with plans, targets, and internal thresholds to identify improvement opportunities and strengthen execution.
Analyze greenhouse footprint, capacity, occupancy, labor demand and availability, planting schedules, cycle duration, environmental conditions, and seasonal constraints.
Track actual versus planned performance to identify risks within controlled-environment operations and external dependencies such as genotyping.
Integrate data sources into decision-support tools that enable scenario modeling and what-if simulations to optimize operations and increase productivity.
Establish data-quality standards, cross-check mechanisms, and automated controls to identify, prevent, and correct data collection, recording, transformation, and analytical errors.
Ensure data integrity, consistency, and end-to-end traceability across digital workflows.
Analyze controlled-environment and laboratory data to identify performance trends, support structured root-cause analysis, and establish data-driven productivity targets.
Translate analytical findings into actionable improvement priorities and measurable performance expectations.
Provide concise, action-oriented insights and recommendations from complex controlled-environment and operational data.
Apply AI tools and applications to identify patterns, generate predictive insights, evaluate scenarios, and strengthen data-driven decision-making.
Support optimization of footprint utilization, resource allocation, project execution, risk mitigation, and digital safeguards that reduce product vulnerabilities.